cell.ME: immunoClust EM-iteration on Cell-events given initial Cluster...

In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

Description

Performs an EM-iteration on cell event observations given an initial cluster membership for the cell events and returns the fitted cluster information in an object of class immunoClust.

Usage

cell.ME(data, parameters=NULL, expName="immunoClust Experiment", 
        history=NULL, state=NULL, label, B=50, tol=1e-5, modelName="mvt")

Arguments

data	A numeric matrix, data frame of observations, or object of class flowFrame.
parameters	A character vector specifying the parameters (columns) to be included in clustering. When it is left unspecified, all the parameters will be used.
expName	The name of the clustering experiment.
history	experimental; unused so far.
state	experimental: unused so far.
label	The N-dimensional vector containing the initial cluster membership. A label-number of 0 for an event indicates that this event is not initially assigned to a cluster.
B	The maximum number of EMt-iterations.
tol	The tolerance used to assess the convergence of the EMt-algorithms.
modelName	Used mixture model; either "mvt" or "mvn" for a t- or Gaussian mixture model respectively.

Value

The fitted clusters information in an object of class immunoClust.

Author(s)

Till Sörensen [e-mail]

References

Sörensen, T., Baumgart, S., Durek, P., Grützkau, A. and Häupl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).

See Also

cell.EM

Examples

data(dat.fcs)
data(dat.exp)
## cell.clustering result for dat.fcs
r1 <- dat.exp[[1]]
summary(r1)
## apply model parameter to all (unfiltered) events
dat.trans <- trans.ApplyToData(r1, dat.fcs)
r2 <- cell.ME(dat.trans, parameters(r1), label=label(r1))
summary(r2)

immunoClust documentation built on Nov. 8, 2020, 5:19 p.m.